CN115440380A - Big data risk identification method and system for intelligent medical service - Google Patents
Big data risk identification method and system for intelligent medical service Download PDFInfo
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Abstract
The invention relates to an intelligent medical technology, and discloses a big data risk identification method aiming at intelligent medical service, which comprises the following steps: acquiring a patient to be diagnosed and medical record calling data, extracting disease category associated data of the patient to be diagnosed, calculating data association degree of the medical record calling data and the disease category associated data, calculating time difference degree of calling time of the medical record calling data and latest re-diagnosis time, calculating data risk index of the patient by using a risk identification formula according to the data association degree and the time difference degree, counting data risk indexes of all patients in a medical record big data database to obtain comprehensive data risk index, and calculating risk index of the medical record big data database according to a big data risk index calculation formula and the comprehensive data risk index. The invention aims to solve the problem that the medical data of the current intelligent medical treatment has high leakage risk.
Description
Technical Field
The invention relates to the technical field of intelligent medical treatment, in particular to a big data risk identification method and system for intelligent medical service and electronic equipment.
Background
Intelligent medical treatment (WITMED for short) is a medical service mode with patient data as a center, and integrates business processes among hospitals, so that regional medical resources are optimized and reasonable allocation of medical resources is realized.
The intelligent medical treatment is to store detailed medical data of patients such as treatment, examination, diagnosis, treatment, prescription, medical advice and course record uniformly, and realize resource sharing of the medical data, so that uniform planning, supervision, evaluation and decision making of the treatment can be carried out among all levels of medical institutions. However, the medical data of the current smart medical treatment has the problem of high leakage risk because the risk of leakage of the purchased medical data is greatly increased due to the high centralization of the medical data of the patient
Disclosure of Invention
The invention provides a big data risk identification method, a big data risk identification device, big data risk identification equipment and a big data risk storage medium for intelligent medical service, and mainly aims to solve the problem that the medical data of the current intelligent medical service is high in leakage risk.
In order to achieve the above object, the present invention provides a big data risk identification method for intelligent medical services, including:
receiving disease category associated data demarcated by a user for each disease category in a pre-constructed disease category set;
constructing a disease category associated data query table according to the corresponding relation between the disease category and the disease category associated data;
acquiring a disease species to be diagnosed of a patient and medical record calling data, and extracting disease species correlation data of the disease species to be diagnosed from the disease species correlation data query table;
calculating the data association degree of the medical record calling data and the disease category association data of the disease category to be diagnosed by using a pre-constructed data association degree calculation formula;
acquiring the latest re-diagnosis time of the patient, and calculating the time difference between the calling time of the medical record calling data and the latest re-diagnosis time;
calculating the data risk index of the patient by utilizing a pre-constructed risk identification formula according to the data association degree and the time difference degree, wherein the risk identification formula is as follows:
wherein N is i Data risk index representing the ith patient, a represents the data relevancy weight, c i Data association degree of the ith patient, a time difference degree weight, and t i Representing the time difference degree of the ith patient;
counting the data risk indexes of all patients in a pre-constructed medical record big data database to obtain a comprehensive data risk index;
calculating the risk index of the medical record big data database according to a pre-constructed big data risk index calculation formula and the comprehensive data risk index, wherein the big data risk index calculation formula is as follows:
wherein M represents the big data risk index and j represents all patient numbers recorded in the medical record big data database.
Optionally, the acquiring data of the patient to be diagnosed and medical record calling data includes:
acquiring the to-be-diagnosed disease species and medical record calling data of a patient, comprising the following steps of:
receiving a disease species to be diagnosed selected by a user in the disease species set;
recording medical record examination information called by a user in a pre-constructed intelligent medical platform;
determining medical record calling items according to the medical record checking information;
and saving the medical record calling items and the calling time corresponding to the medical record calling items to obtain the medical record calling data.
Optionally, the extracting, from the disease category correlation data lookup table, the disease category correlation data of the disease category to be diagnosed includes:
taking the disease species to be diagnosed as a disease species correlation index;
extracting disease category correlation items of the disease category to be diagnosed from the disease category correlation data query table by using the disease category correlation index;
extracting disease species correlation weights of the disease species correlation items and the disease species to be diagnosed;
and establishing a corresponding relation between the disease category correlation item and the disease category correlation weight to obtain the disease category correlation data.
Optionally, the calculating, by using a pre-constructed data association degree calculation formula, a data association degree between the medical record calling data and the disease category association data of the disease category to be diagnosed includes:
judging the item coincidence number of the disease category calling item in the medical record calling data and the disease category correlation item in the disease category correlation data of the disease category to be diagnosed;
and calculating the data association degree according to the project coincidence number and the data association degree calculation formula.
Optionally, the data association degree calculation formula is as follows:
wherein Q represents the number of items overlapped between the disease category retrieval item and the disease category related item, and β q The disease category related weight of the q-th disease category overlapped item representing the overlap, P the item number of the disease category calling item, P the total item number of the disease category calling item, alpha p The disease category-associated weight of the p-th disease category retrieval item is represented.
Optionally, the calculating a time difference between the calling time of the medical record calling data and the latest review time includes:
extracting the calling date of the medical record calling data and the revising date of the latest revising time;
and calculating the time difference by using a pre-constructed difference calculation formula according to the calling date and the re-diagnosis date.
Optionally, the calculation formula of the difference degree is as follows:
t i ＝(|F i -D i |) k
wherein, F i Indicating the date of the return visit, D i Indicating the date of retrieval and k the discrepancy index.
Optionally, after the risk index of the medical record big data database is calculated according to the pre-constructed big data risk index calculation formula and the comprehensive data risk index, the method further includes:
defining preset risk index value fields corresponding to different risk grades;
and judging a target risk index value domain to which the risk index of the medical record big data database belongs, and judging the risk grade of the medical record big data database according to the target risk index value domain.
In order to solve the above problems, the present invention also provides a big data risk identification system for intelligent medical services, the system including:
the disease category associated data query table construction module is used for receiving disease category associated data defined by a user for each disease category in the pre-constructed disease category set; constructing a disease category associated data query table according to the corresponding relation between the disease category and the disease category associated data;
the data association degree calculation module is used for acquiring the disease type to be diagnosed and medical record calling data of the patient and extracting the disease type association data of the disease type to be diagnosed from the disease type association data query table; calculating the data association degree of the medical record calling data and the disease category association data of the disease category to be diagnosed by using a pre-constructed data association degree calculation formula;
the time difference degree calculation module is used for acquiring the latest re-diagnosis time of the patient and calculating the time difference degree between the calling time of the medical record calling data and the latest re-diagnosis time;
a data risk index calculation module, configured to calculate a data risk index of the patient according to the data association degree and the time difference degree by using a pre-constructed risk identification formula, where the risk identification formula is as follows:
wherein N is i Representing the data risk index of the ith patient, a representing the weight of the relevance of the data, c i Data relevancy representing the ith patient, a representing time difference weight, t i Representing the time difference degree of the ith patient;
the comprehensive data risk index calculation module is used for counting the data risk indexes of all patients in a pre-constructed medical record big data database to obtain a comprehensive data risk index;
the big data risk index calculation module is used for calculating the risk index of the medical record big data database according to a pre-constructed big data risk index calculation formula and the comprehensive data risk index, wherein the big data risk index calculation formula is as follows:
wherein M represents the big data risk index and j represents all patient numbers recorded in the medical record big data database.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the big data risk identification method for intelligent medical services described above.
According to the method, the data association degree of the medical record calling data of the patient and the disease category associated data of the disease category to be diagnosed of the patient is calculated, the content accuracy of the medical record calling data is embodied through the data association degree, the time accuracy of the medical record calling data is embodied through the time difference degree of the latest re-diagnosis time and the calling time of the medical record calling data, the data risk index of the patient is calculated according to the data association degree and the time difference degree through the risk identification formula, and finally the risk index of the medical record big data database is obtained by averaging the data risk indexes of all the patients in the medical record big data database. Therefore, the big data risk identification method for the intelligent medical service provided by the embodiment of the invention can solve the problem that the medical data of the current intelligent medical service has high leakage risk.
Drawings
Fig. 1 is a schematic flowchart illustrating a big data risk identification method for intelligent medical services according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a big data risk identification apparatus for intelligent medical services according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device implementing the big data risk identification method for intelligent medical services according to an embodiment of the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a big data risk identification method for intelligent medical service. In the embodiment of the present application, the execution subject of the big data risk identification method for intelligent medical services includes, but is not limited to, at least one of electronic devices, such as a server and a terminal, which can be configured to execute the method provided in the embodiment of the present application. In other words, the big data risk identification method for the intelligent medical service may be performed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Referring to fig. 1, a schematic flow chart of a big data risk identification method for intelligent medical services according to an embodiment of the present invention is shown. In this embodiment, the big data risk identification method for intelligent medical services includes steps S1 to S8:
s1, receiving disease category associated data demarcated by a user for each disease category in a pre-constructed disease category set.
Explicably, the set of disease species refers to the set of disease species names, such as: cold, chronic bronchitis, asthma, acute gastritis, nephrolithiasis, myocarditis and esophageal cancer. The disease category associated data refers to physiological data associated with various diseases, such as: the disease species related data of the acute gastritis comprise blood conventional data, gastroscopy data, CT (computed tomography) examination data and the like; the data related to asthma disease species include respiratory function test data, blood gas analysis data, chest X-ray test data, complement test data of specific allergen, and the like.
It is understood that the intelligent medical treatment (WITMED) is a regional medical information platform centered on an electronic health record, and the electronic health record may include sensitive data such as the name, telephone number, physical condition, and drug usage of a patient, and the medical data of the patient is integrated and shared to construct a comprehensive professional medical network.
S2, constructing a disease category associated data query table according to the corresponding relation between the disease category and the disease category associated data.
Explainably, the disease category associated data lookup table refers to a table for recording the affiliation relationship between the disease category and the disease category associated data.
In the embodiment of the invention, the disease category correlation data query table can query corresponding items to be checked according to the disease category to be diagnosed.
And S3, acquiring the disease species to be diagnosed of the patient and medical record calling data, and extracting the disease species correlation data of the disease species to be diagnosed from the disease species correlation data query table.
In detail, the disease type to be diagnosed refers to the disease type to be examined and treated by the patient, and can be selected from a group of diseases in the intelligent medical platform by a doctor.
Interpretably, the medical record calling data refers to data that a doctor consults on the intelligent medical platform, such as: when the disease to be diagnosed is asthma, a doctor inquires medical record examination information related to asthma on the intelligent medical platform, such as: respiratory function test data, blood gas analysis data, chest X-ray test data, and the like.
In the embodiment of the present invention, the acquiring of the patient to be diagnosed and the medical record calling data includes: acquiring the to-be-diagnosed disease species and medical record calling data of a patient, comprising the following steps of:
receiving a disease species to be diagnosed selected by a user in the disease species set;
recording medical record examination information called by a user in a pre-constructed intelligent medical platform;
determining medical record calling items according to the medical record checking information;
and saving the medical record calling items and the calling time corresponding to the medical record calling items to obtain the medical record calling data. The medical record retrieval item refers to an examination item to which the medical record examination information belongs, and includes, for example: the respiratory function check data belongs to a respiratory function check item; the blood gas analysis data belongs to a blood gas analysis project.
In an embodiment of the present invention, the extracting disease category associated data of the disease category to be diagnosed from the disease category associated data lookup table includes:
taking the disease species to be diagnosed as a disease species correlation index;
extracting disease category correlation items of the disease category to be diagnosed from the disease category correlation data query table by using the disease category correlation index;
extracting disease species correlation weights of the disease species correlation items and the disease species to be diagnosed;
and establishing a corresponding relation between the disease category correlation item and the disease category correlation weight to obtain the disease category correlation data.
Explainably, when the disease species to be diagnosed is obtained, the disease species correlation items related to the disease species to be diagnosed can be indexed according to the disease species correlation data query table.
And S4, calculating the data association degree of the medical record calling data and the disease category association data of the disease category to be diagnosed by using a pre-constructed data association degree calculation formula.
It can be understood that the data association degree refers to the overlapping degree of the medical record calling item of the medical record calling data and the same item in the disease category association item of the disease category association data, and the more the same item is overlapped, the greater the data association degree is.
Explicably, the data association degree calculation formula is as follows:
wherein Q represents the number of items overlapped between the disease category retrieval item and the disease category related item, and β q The disease category related weight of the q-th disease category overlapped item representing the overlap, P the item number of the disease category calling item, P the total item number of the disease category calling item, alpha p The disease category-associated weight of the p-th disease category retrieval item is expressed.
It should be understood that the disease category association weight refers to the importance degree of the disease category coincidence item for the disease category to be diagnosed, such as: when the disease category to be diagnosed is asthma, the importance degree of the disease category retrieval items are a respiratory function examination item, a blood gas analysis item, a chest X-ray examination item and the like in sequence, and the corresponding weights can be 0.3, 0.2 and 0.15 in sequence.
Explainably, the data relevance degree refers to the weight ratio of the disease category coincidence item to the disease category retrieval item, and the importance of different disease category coincidence items can be reflected by giving a relatively important disease category coincidence item a larger weight, so that the accuracy and the specialty of the doctor retrieval item are reflected. The accuracy of data retrieval can be calculated by calculating the data relevancy of data retrieval of relevant personnel such as doctors, and the data relevancy is smaller when the number of disease overlapping items is smaller. And data stealing behaviors such as general data leakage and the like do not distinguish the data, so that whether data are called by a professional doctor or data are called when the data are stolen can be effectively distinguished through the data association degree.
In an embodiment of the present invention, the calculating the data association degree between the medical record calling data and the disease category association data of the disease category to be diagnosed by using a pre-constructed data association degree calculation formula includes:
judging the item coincidence number of the disease category calling item in the medical record calling data and the disease category correlation item in the disease category correlation data of the disease category to be diagnosed;
and calculating the data association degree according to the project coincidence number and the data association degree calculation formula.
And S5, acquiring the latest re-diagnosis time of the patient, and calculating the time difference between the calling time of the medical record calling data and the latest re-diagnosis time.
It should be understood that the latest review time refers to the review time established in the previous treatment. The time difference degree refers to a difference degree calculated based on the number of days that the calling time is different from the latest review time.
In an embodiment of the present invention, the calculating a time difference between the calling time of the medical record calling data and the latest review time includes:
extracting the calling date of the medical record calling data and the re-diagnosis date of the latest re-diagnosis time;
and calculating the time difference degree by utilizing a pre-constructed difference degree calculation formula according to the calling date and the re-diagnosis date.
In detail, the disparity calculation formula is as follows:
t i ＝(|F i -D i |) k
wherein, F i Indicating the date of the return visit, D i Indicating the date of retrieval and k the discrepancy index.
Alternatively, the index of difference may be 2, with the greater the number of days apart, the greater the degree of difference, for example: when the date of the return visit is 1 month and 1 day, and the date of the calling is 1 month and 5 days, the difference degree is 16. The farther from the prescribed review date, the less likely it is that the medical record retrieval data will be retrieved by a professional such as a doctor.
And S6, calculating the data risk index of the patient by using a pre-constructed risk identification formula according to the data association degree and the time difference degree.
In detail, the risk identification formula is as follows:
wherein, N i Data risk index representing the ith patient, a represents the data relevancy weight, c i Data relevancy representing the ith patient, a representing time difference weight, t i Representing the time difference of the ith patient.
In the embodiment of the present invention, the data association degree weight and the time difference degree weight may be adjusted according to time requirements, but the sum of the data association degree weight and the time difference degree weight is 1. The data association degree weight may be 0.7, and the time difference degree weight may be 0.3. The larger the data risk index, the higher the risk of data leakage.
Understandably, the value range of the data risk index is 0-1, when the time difference degree of the exponential function is a positive value, the larger the time difference degree is, the larger the value of the exponential function is, but the maximum value is 1.
It should be understood that by comprehensively considering the data association degree of the medical record calling data and the time difference degree of the medical record calling time, the medical record data calling behavior of professionals such as doctors can be effectively distinguished from the data stealing behavior of illegal persons, and when data leakage occurs, the leaked data content and the leaked time cannot be screened according to the type of the disease to be diagnosed and the time for the follow-up diagnosis.
And S7, counting the data risk indexes of all patients in the pre-constructed medical record big data database to obtain a comprehensive data risk index.
In the embodiment of the invention, the comprehensive risk index can be obtained by accumulating the data risk indexes of all patients in the medical record big data database.
And S8, calculating the risk index of the medical record big data database according to a pre-constructed big data risk index calculation formula and the comprehensive data risk index.
In detail, the big data risk index calculation formula is as follows:
wherein M represents the big data risk index and j represents all patient numbers recorded in the medical record big data database.
Explicably, the average risk index of the medical record big data database can be obtained by averaging the comprehensive risk indexes, and the average risk index can be used as the risk index evaluation value of the medical record big data database by averaging the risk indexes.
It should be understood that the risk index of the medical record big data base can be calculated not only by the mean value, but also by the indexes such as the mode and median of the data risk indexes of all patients, for example: when the mode of the data risk index is 0.8, the risk that data leaks is high; when the median of the data risk index is 0.2, the risk of data leakage is low.
In an embodiment of the present invention, after calculating the risk index of the medical record big data database according to the pre-constructed big data risk index calculation formula and the comprehensive data risk index, the method further includes:
defining preset risk index value fields corresponding to different risk grades;
and judging a target risk index value field to which the risk index of the medical record big data database belongs, and judging the risk level of the medical record big data database according to the target risk index value field.
Explainably, the risk of data leakage of the big medical record database can be evaluated hierarchically according to the numerical value of the risk index of the big medical record database, for example: when the risk level is 0-0.3, the risk level may be a low-level risk; when the risk rating is 0.3-0.6, the risk rating may be an intermediate risk; when the risk level is 0.6-1.0, the risk level can be high, and at the moment, the data of the medical record big data database can be preliminarily judged to be illegally stolen, so that the risk of data leakage exists.
According to the method, the data association degree of the medical record calling data of the patient and the disease type association data of the disease type to be diagnosed of the patient is calculated, the content accuracy of the medical record calling data is further embodied through the data association degree, the time accuracy of the medical record calling data is further embodied through the time difference degree of the latest re-diagnosis time and the calling time of the medical record calling data, the data risk index of the patient is calculated according to the data association degree and the time difference degree through the risk identification formula, and finally the risk index of the medical record big data database is obtained by averaging the data risk indexes of all the patients in the medical record big data database. Therefore, the big data risk identification method for the intelligent medical service provided by the embodiment of the invention can solve the problem that the medical data of the current intelligent medical service has high leakage risk.
Fig. 2 is a functional block diagram of a big data risk identification apparatus for intelligent medical services according to an embodiment of the present invention.
The big data risk identification system 100 for the intelligent medical service according to the present invention may be installed in an electronic device. According to the realized functions, the big data risk identification system 100 for the intelligent medical service may include a disease category associated data lookup table construction module 101, a data association degree calculation module 102, a time difference degree calculation module 103, a data risk index calculation module 104, a comprehensive data risk index calculation module 105, and a big data risk index calculation module 106. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform a fixed function, and are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the disease category associated data lookup table construction module 101 is configured to receive disease category associated data defined by a user for each disease category in a pre-constructed disease category set; constructing a disease category associated data query table according to the corresponding relation between the disease category and the disease category associated data;
the data association degree calculation module 102 is configured to acquire disease types to be diagnosed of a patient and medical record calling data, and extract disease type association data of the disease types to be diagnosed from the disease type association data lookup table; calculating the data association degree of the medical record calling data and the disease category association data of the disease category to be diagnosed by using a pre-constructed data association degree calculation formula;
the time difference degree calculation module 103 is configured to obtain the latest review time of the patient, and calculate a time difference degree between the time of retrieving the medical record retrieval data and the latest review time;
the data risk index calculation module 104 is configured to calculate a data risk index of the patient according to the data association degree and the time difference degree by using a pre-constructed risk identification formula, where the risk identification formula is as follows:
wherein, N i Representing the data risk index of the ith patient, a representing the weight of the relevance of the data, c i Data relevancy representing the ith patient, a representing time difference weight, t i Time of day of the ith patientThe degree of difference between them;
the comprehensive data risk index calculation module 105 is used for counting the data risk indexes of all patients in a pre-constructed medical record big data database to obtain a comprehensive data risk index;
the big data risk index calculation module 106 is configured to calculate a risk index of the medical record big data database according to a pre-constructed big data risk index calculation formula and the comprehensive data risk index, where the big data risk index calculation formula is as follows:
wherein M represents the big data risk index and j represents all patient numbers recorded in the medical record big data database.
In detail, in the embodiment of the present application, each module in the big data risk identification system for smart medical services 100 adopts the same technical means as the big data risk identification method for smart medical services described in fig. 1, and can produce the same technical effect, and is not described herein again.
Fig. 3 is a schematic structural diagram of an electronic device 1 for implementing a big data risk identification method for intelligent medical services according to an embodiment of the present invention.
The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as a big data risk identification method program for intelligent medical services.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a big data risk identification method program for smart medical services, and the like) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to store not only application software installed in the electronic device and various types of data, such as codes of big data risk identification method programs for intelligent medical services, but also data that has been output or will be output temporarily.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device 1 and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The big data risk identification method program for intelligent medical services stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when running in the processor 10, the method can realize that:
receiving disease category associated data demarcated by a user for each disease category in a pre-constructed disease category set;
constructing a disease category associated data query table according to the corresponding relation between the disease category and the disease category associated data;
acquiring a disease species to be diagnosed of a patient and medical record calling data, and extracting disease species correlation data of the disease species to be diagnosed from the disease species correlation data query table;
calculating the data association degree of the medical record calling data and the disease category association data of the disease category to be diagnosed by using a pre-constructed data association degree calculation formula;
acquiring the latest re-diagnosis time of the patient, and calculating the time difference between the calling time of the medical record calling data and the latest re-diagnosis time;
calculating the data risk index of the patient by utilizing a pre-constructed risk identification formula according to the data association degree and the time difference degree, wherein the risk identification formula is as follows:
wherein, N i Representing the data risk index of the ith patient, a representing the weight of the relevance of the data, c i Data association degree of the ith patient, a time difference degree weight, and t i Representing the time difference degree of the ith patient;
counting the data risk indexes of all patients in a pre-constructed medical record big data database to obtain a comprehensive data risk index;
calculating the risk index of the medical record big data database according to a pre-constructed big data risk index calculation formula and the comprehensive data risk index, wherein the big data risk index calculation formula is as follows:
wherein M represents the big data risk index, and j represents the number of all patients recorded in the big data database of the medical record.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor of an electronic device, implements:
receiving disease category associated data demarcated by a user for each disease category in a pre-constructed disease category set;
constructing a disease category associated data query table according to the corresponding relation between the disease category and the disease category associated data;
acquiring a disease species to be diagnosed of a patient and medical record calling data, and extracting disease species correlation data of the disease species to be diagnosed from the disease species correlation data query table;
calculating the data association degree of the medical record calling data and the disease category association data of the disease category to be diagnosed by using a pre-constructed data association degree calculation formula;
acquiring the latest re-diagnosis time of the patient, and calculating the time difference between the calling time of the medical record calling data and the latest re-diagnosis time;
calculating the data risk index of the patient by utilizing a pre-constructed risk identification formula according to the data association degree and the time difference degree, wherein the risk identification formula is as follows:
wherein N is i Representing the data risk index of the ith patient, a representing the weight of the relevance of the data, c i Data association degree of the ith patient, a time difference degree weight, and t i Representing the time difference degree of the ith patient;
counting the data risk indexes of all patients in a pre-constructed medical record big data database to obtain a comprehensive data risk index;
calculating the risk index of the medical record big data database according to a pre-constructed big data risk index calculation formula and the comprehensive data risk index, wherein the big data risk index calculation formula is as follows:
wherein M represents the big data risk index and j represents all patient numbers recorded in the medical record big data database.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A big data risk identification method for intelligent medical services, the method comprising:
receiving disease category associated data demarcated by a user for each disease category in a pre-constructed disease category set;
constructing a disease category associated data query table according to the corresponding relation between the disease category and the disease category associated data;
acquiring a disease species to be diagnosed of a patient and medical record calling data, and extracting disease species correlation data of the disease species to be diagnosed from the disease species correlation data query table;
calculating the data association degree of the medical record calling data and the disease category association data of the disease category to be diagnosed by using a pre-constructed data association degree calculation formula;
acquiring the latest review time of the patient, and calculating the time difference between the retrieval time of the medical record retrieval data and the latest review time;
calculating the data risk index of the patient by utilizing a pre-constructed risk identification formula according to the data association degree and the time difference degree, wherein the risk identification formula is as follows:
wherein N is i Representing the data risk index of the ith patient, a representing the weight of the relevance of the data, c i Data relevancy representing the ith patient, a representing time difference weight, t i Representing the time difference degree of the ith patient;
counting the data risk indexes of all patients in a pre-constructed medical record big data database to obtain a comprehensive data risk index;
calculating the risk index of the medical record big data database according to a pre-constructed big data risk index calculation formula and the comprehensive data risk index, wherein the big data risk index calculation formula is as follows:
wherein M represents the big data risk index, and j represents the number of all patients recorded in the big data database of the medical record.
2. The big data risk identification method for intelligent medical services as claimed in claim 1, wherein said obtaining the patient's to-be-diagnosed disease category and medical record calling data comprises:
receiving a disease seed to be diagnosed selected by a user in the disease seed set;
recording medical record examination information called by a user in a pre-constructed intelligent medical platform;
determining medical record calling items according to the medical record checking information;
and saving the medical record calling items and the calling time corresponding to the medical record calling items to obtain the medical record calling data.
3. The big data risk identification method for intelligent medical services as claimed in claim 2, wherein said extracting the disease category associated data of said disease category to be diagnosed from said disease category associated data lookup table comprises:
taking the disease species to be diagnosed as a disease species association index;
extracting disease category correlation items of the disease category to be diagnosed from the disease category correlation data query table by using the disease category correlation index;
extracting disease species correlation weights of the disease species correlation items and the disease species to be diagnosed;
and establishing a corresponding relation between the disease category correlation item and the disease category correlation weight to obtain the disease category correlation data.
4. The big data risk identification method for intelligent medical services as claimed in claim 3, wherein said calculating the data association degree of the medical record calling data and the patient-related data of the patient to be diagnosed by using the pre-constructed data association degree calculation formula comprises:
judging the item coincidence number of the disease category calling item in the medical record calling data and the disease category correlation item in the disease category correlation data of the disease category to be diagnosed;
and calculating the data association degree according to the project coincidence number and the data association degree calculation formula.
5. The big data risk identification method for intelligent medical services as claimed in claim 4, wherein the data association degree calculation formula is as follows:
wherein Q represents the number of items overlapped between the disease category retrieval item and the disease category related item, and β q The disease category associated weight of the q-th disease category overlapping item representing overlapping, P represents the item number of the disease category calling item, P represents the total item number of the disease category calling item, and alpha p The disease category-associated weight of the p-th disease category retrieval item is represented.
6. The big data risk identification method for intelligent medical services as claimed in claim 2, wherein said calculating the time difference between the calling time of the medical record calling data and the latest review time comprises:
extracting the calling date of the medical record calling data and the revising date of the latest revising time;
and calculating the time difference degree by utilizing a pre-constructed difference degree calculation formula according to the calling date and the re-diagnosis date.
7. The big data risk identification method for intelligent medical services as claimed in claim 6, wherein the calculation formula of the difference degree is as follows:
t i ＝(|F i -D i |) k
wherein, F i Indicating the date of the return visit, D i Denotes the date of retrieval, k denotes the variance index。
8. The big data risk identification method for intelligent medical services according to claim 1, wherein after calculating the risk index of the medical record big data database according to the pre-constructed big data risk index calculation formula and the comprehensive data risk index, the method further comprises:
defining preset risk index value fields corresponding to different risk grades;
and judging a target risk index value field to which the risk index of the medical record big data database belongs, and judging the risk level of the medical record big data database according to the target risk index value field.
9. A big data risk identification system for intelligent medical services, the system comprising:
the disease category associated data query table construction module is used for receiving disease category associated data defined by a user for each disease category in the pre-constructed disease category set; constructing a disease category associated data query table according to the corresponding relation between the disease category and the disease category associated data;
the data association degree calculation module is used for acquiring the disease species to be diagnosed and medical record calling data of the patient and extracting the disease species association data of the disease species to be diagnosed from the disease species association data query table; calculating the data association degree of the medical record calling data and the disease category association data of the disease category to be diagnosed by using a pre-constructed data association degree calculation formula;
the time difference degree calculation module is used for acquiring the latest re-diagnosis time of the patient and calculating the time difference degree between the calling time of the medical record calling data and the latest re-diagnosis time;
a data risk index calculation module, configured to calculate a data risk index of the patient according to the data association degree and the time difference degree by using a pre-constructed risk identification formula, where the risk identification formula is as follows:
wherein, N i Representing the data risk index of the ith patient, a representing the weight of the relevance of the data, c i Data association degree of the ith patient, a time difference degree weight, and t i Representing the time difference degree of the ith patient;
the comprehensive data risk index calculation module is used for counting the data risk indexes of all patients in a pre-constructed medical record big data database to obtain a comprehensive data risk index;
the big data risk index calculation module is used for calculating the risk index of the medical record big data database according to a pre-constructed big data risk index calculation formula and the comprehensive data risk index, wherein the big data risk index calculation formula is as follows:
wherein M represents the big data risk index and j represents all patient numbers recorded in the medical record big data database.
10. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the big data risk identification method for intelligent medical services according to any one of claims 1 to 8.
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